@inproceedings{agarwal-etal-2026-deterministic,
    title = "A Deterministic Multi-Stage Retrieval Pipeline for Longitudinal {EHR} Question Answering",
    author = "Agarwal, Shubham  and
      Searle, Thomas  and
      Dobson, Richard  and
      Majkic, Ninoslav  and
      Moller-Grell, Niko",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.53/",
    pages = "665--678",
    ISBN = "979-8-89176-434-7",
    abstract = "Retrieval-augmented generation (RAG) holds promise for clinical question answering over electronic health records (EHRs), but existing systems treat retrieval as an opaque subroutine, limiting auditability and reliability in patient care workflows. We introduce a deterministic multi-stage retrieval pipeline for longitudinal EHR question answering that decomposes retrieval into four distinct, ablated stages where each stage is instrumented with diagnostic metrics, making the flow of clinical evidence measurable and auditable at every step. Evaluated on a broad LLM-annotated cohort and an expert-annotated cardiovascular benchmark developed alongside clinicians from real ICU records, the full pipeline achieves 22-23{\%} relative recall gain over a strong dense retrieval baseline across both cohorts, with consistent improvements in downstream answer quality. The pipeline{'}s deterministic and transparent design addresses a critical gap in clinical NLP: retrieval systems that clinicians and researchers can not only rely on, but inspect, audit, and build upon for real-world deployment."
}